VTT TECHNICAL RESEARCH CENTRE OF FINLAND LTD

Sensing Technology for

Food and Agriculture

Philippe Monnoyer, Ph.D.

Sensors in Food and Agriculture, 29-30.11.2016, Møller Centre, Cambridge, UK

VTT Ltd Technical Research Center of Finland

Finland

33

Finland in the world

• Highest Social

Progress Index

(2016)

• Safest Country (2016)

• Greenest (EPI 2016)

• Most powerful

passport (2014)

• Best european intnl

student satisfaction

(2014)

• Least gender gap

(2014)

• OECD education

ranking (2010)• Least corruption

(2014)

• Helsinki quality of

life (2013)

• 4th: Global

Innovation Index

• …

st

2

nd

3d

44

Main exports: Electrotechnical goods, metal products,

machinery, transport equipment, wood and paper products,

chemicals

99 years old RepublicPresident Mr Sauli Niinistö

5.5Millions (18/km2)

Member 1955

Member 1995-40.7 C/-40.7 F +32.8 C/91 F

5

DATA

2013

VTT LtdTechnical Research Centre of Finland

730.12.16 7

BASIC RESEARCH

APPLIED RESEARCH

DEVELOPMENT

UNIVERSITIES

INDUSTRY

VTT

VTT’s status as performer of R&D work

830.12.16 8

VTT Technical Research Centre of

Finland Ltd

Net turnover and other operating

income 272 M€ for VTT Group in 2015(VTT Group’s turnover 185 M€ in 2015)

Personnel 2,470 (VTT Group 31.12.2015 )

Unique research and testing

infrastructure

Wide national and international

cooperation network

We use

4 million

hours of brainpower a

year to develop

new technological

solutions

36% of Finnish

innovations

include VTT

expertise

TOP 2VTT is second

most active

patenting

organisation in

Finland (2014) A leading R&D organisation

in Nordic countries

We provide expert services

for our domestic and

international customers

and partners, both in

private and public sectors

Click to edit Master title style

Click to edit Master text styles

Second level

Third level

Fourth level

Fifth level

930.12.16 9

Some VTT spin-offs and start-ups

10

Agriculture

1130.12.16 11

Top Agricultural Products

12

Milk consumption

1330.12.16 13

1000 €More income

for farmersEach cow, Each year

Milk Quality

1430.12.16 14

HOW?Selection

Food

Health

Reproduction and fertility

…

Hay

Artturi Virtanen: Nobel prize in Chemistry 1945

"Artturi Virtanen - Facts". Nobelprize.org. Nobel Media AB 2014. Web. 24 Nov 2016.

<http://www.nobelprize.org/nobel_prizes/chemistry/laureates/1945/virtanen-facts.html>

1730.12.16 17

Fodder

10,000

Barn fires

Total damages

Billions

Quanturi is the forerunner of the Farming IoT

19

Alerts

Cloud service

Visualization

The Quanturi solution

20

Sensor probes

Reader

Quanturi is the prize winner of

the Innov’Space from the

international agricultural fair in

France in September 2016

21

Haytech by Quanturi video https://goo.gl/bpMkUg

Measuring

Milk

Quality

2323

Spectrometer Microspectrometer

Fragile

Heavy

Big

Expensive

Robust

Light

Small

Cheap

2430.12.16 24

Existing MEMS or Piezo FPI technology platforms

0,2 0,3 µm0,4 0,6 0,8 1 2 3 4 5 6 7 8 10 11 12 13 14

VisibleUV NIR SWIR LWIRMWIR

CMOS & CCD Image Sensors

InGaAs

InSb

MCT

0,82-1,05

PbSe

0,40-0,58

0,50

1,5-2,0

1,7-2,2

1,95

1,3-1,7

1,77

1,5

3,0-3,7

2,8-3,4

3,3

3,3

3,2-4,0

4,0-5,03,3

4,2

8,5-10,7

7,3-9,6

9,3

9,3

0.4-1.0 1.0-2.5

0.50-0.85

Material science

Biology

Medical, Anticounterfeit,

Space, Remote sensing, PoC

Pharma,

Food,

Agriculture

Gases,

Plastics MineralsMinerals

Forestry

4.0-5.0

Poly-Si-SiO2

0,58-0,80 0,950,36-0,50

0,47-0,65

0,52-0,71

5.5-7.5

PbTe-ZnSe

7.0-11.0

PbTe-ZnSe0.36-0.4

0.29-0.34

Metallic

mirrors, Ti,

Ag

Dielectric

mirrors

1.0-2.5

Poly-Si-SiO2

2530.12.16 25

After ~200,000 hours of R&D …

350 nm

UV

11 µm

TIR

Microspectrometry

& Hyperspectral

Imaging Solutions

from

+ Color Xray based on Medipix

CERN technology!

BUILDS YOUR CUSTOM HSI CAMERA

2630.12.16 26

Spectroscopic milk sensing

Measurement performed with

VTT MEMS based

Microspectrometry

2727

Comparison with ICAR accuracy requirements

ICAR = International Committee for Animal Recording

http://www.icar.org/

In our model, RMSECV for fat and protein weight-% is 0.26 and 0.20,

respectively. These two values can be roughly compared to the

standard deviation limits stated in the table above.

2830.12.16 28

IoT

of

dugs

www.spectralengines.com

Data management, storing,

analysis and sensor fusion

Elisa IoT™

Internet of FarmingDairy business in Europe 150 B€

24 million cows in 1 million farms

75 million agricultural IoT devices by 2020

Improve production efficiency of

farms by monitoring milk quality

Real-time fat and protein

information from individual cows

*) Business Insider, “Why IoT, Big Data & Smart Farming is the Future of Agriculture”, October 2016.

3030

What about undesired substances in milk ?

Antibiotics ?

Antibiotic resistance

Yogurt, cheese preparation affected (200 M€/yr in EU)

Copyright © 2016 by BioMensio

Handheld multi-analyte biosensor platformRobust, Multiplexed and Label-free

SensorSample

Microfluidic cartridge

Label-free

Cost efficient

Digital

Handheld

Robust

Integrated Multi-analyte

Easy to use

Hand held platform

Disposable

cartridge

Reader

UI

Focus:

• Antibiotics in milk and meat juice

• Mycotoxins from grain products

3230.12.16 32

Fungi Fusarium

producing Mycotoxins

in cereal crops

25% of the world's

crops are

affected by

mycotoxins each

year, with annual

losses of around

1 billion metric

tons of foods and

food products.

Testing needed

through the food

supply chain

3330.12.16 33

HappyCow EU project – A FITBIT FOR COWS

Common abnormalities and

behavioural phenomena to be

detected:

Estrus

Diseases

Lameness

Rumination

Acceleration and magnetic

field in 3D >base station

>cloud

433 MHz

x100 modules/base station

300 m range

The system has been developed in cooperation with Connecterra (NL), University of Wageningen (NL) and University of Aarhus (DK)

3430.12.16 34

Prize winning solution – Web summit 2015

Connecterra was chosen as the top startup for 2015 in the

Alpha category.

https://blog.websummit.net/introducing-2015s-best-startups-its-

our-pitch-powered-by-audi-winners/

188,000

lakes in

Finland

3630.12.16 36

Blue-Green Algae• Link to fertilizers

• Several toxins

• Economic impact US only: $75M (Larkin&Adams, 2007)

• Swimming, Inhalation, Swallowing

• Affects fishes (depleted oxygen)

• Serious cases of human and livestock poisoning and deaths in a number of

countries worldwide (e.g., in Brazil, Australia and North America)

Some of the more common toxins produced by blue-green algae can cause allergic-

type reactions such as rashes, eye, nose, throat irritation, asthma, headaches, fever,

nausea, stomach cramps, vomiting and diarrhea

Other toxins can impact internal organs, and can cause gastroenteritis, tissue

damage, muscle weakness and paralysis in severe exposure cases

Central nervous system and can cause seizures, paralysis, respiratory failure or

cardiac arrest

37

Paper based test for toxic Cyanobacteria from

water

• Paper-based test for

consumers to detect toxic

cyanobacteria

• Paper based diagnostic

platform

• Printed fluidic channels

on paper (VTTs IPR)

• Inkjet deposited

biomolecules

• Adjustable platform which

can be adapted for various

detection chemistries

38

Mapping of

the

cyanobacteri

a in the lake

Lohjanjärvi.

Project HSI-

stereo in

collaboration

with

University of

Jyväskylä,

FGI, Luode

consulting,

Lentokuva

Vallas

Image © Ilkka

Pölönen,

Anna-Leena

Erkkilä

Or with Hyperspectral Imaging

© Natural Resources Institute Finland

Precision fertilization task based on hyperspectral imaging

39 30.12.2016

1000*

1000

*35.5 442.0 BiomassNitrogen

BiomassNNEED

www.dronefinland.fi

40

Forests

Sources: Forest Europe: State of Europe’s Forests 2015

100 Millions ha

20% of Finnish exports. 11 b€

4130.12.16 41

Pinus

peuce

Pinus

sylvestris

Abies

koreana

Picea

koreana

Picea

jezoenis

Picea

abies

Pseudotsuga

menziesii

Picea

omorika

Quercus rubra

Examples

of tree

species

Composite of bands with

1578, 925 and 636nm

• 400-1000 nm VNIR camera

• 1100-1600 nm SWIR camera

R.Näsi et al., UAS BASED TREE SPECIES IDENTIFICATION

Processing flow for Hyperspectral data

Field reflectance

reference

Data capture

Image pre-

processing

Image

orientation

DSM

generation

Radiometric

block

adjustment

Individual tree

detection

Spectral

characteristics

of trees

ClassificationMosaic

generation

Forest

reference

plots

ALS DTM

R.Näsi et al., UAS

BASED TREE

SPECIES

IDENTIFICATION

Forest classification

Näsi, R., Honkavaara, E., Lyytikäinen-Saarenmaa, P., Blomqvist, M., Litkey, P.,

Hakala, T., ... & Holopainen, M. (2015). Using UAV-Based Photogrammetry and

Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level. Remote

Sensing, 7(11), 15467-15493Image courtesy Roope Näsi from National Land Survey of Finland

Bark Beetle Outbreaks

Grains

Control crop pricing

Control protein for aninal feed

…

• On site handheld measurement of grains, which can determine fast

and accurately the concentrations of key determinants of harvest

value and processing cost:

• Protein

• Carbohydrates (starch + sugars + fibers)

• Oil

• Moisture

• Positioning (GPS) and wireless internet connection in order to

enable value added services

• Data collection and analysis (“big data”)

• SW products which improve productivity

World First Truly Handheld

46

GrainSense Diffuse reflection Diffuse transmission

Detector + Silicon - InGaAs (1.0 < λ < 2.4µm) + Silicon

% of mass sampled + 100% - Tiny (<<1%) +/- ~50%

Sample presentation error + None +/- Moderate (with mechanics) - Large (”pin holes”)

#Kernels required + 10 to 100 +/- Many thousands - Thousands

Wet sample possible? + Yes +/- Within limits (mech. problems) - No

Optical Penetration: Method Comparison

• GrainSense addresses 1 billion plus

market potential worldwide with a Cloud

based service offering and a hand-held

device

• Pricing very competitive and affordable by

individual farmers

• GrainSense pay back time within one

harvesting season (less than 6 months)

Out in Market 2017

4930.12.16 49

Potato grower’s app by Solentum B.V.

Solentum B.V., a provider of science based tools and services, is an

independent daughter company of HZPC Holland B.V, the world

leader in potato breeding

Solentum B.V. has developed a mobile application to support the

potato growers

By means of a photo taken with the mobile phone, the length, width,

and the measured weight per potato is calculated. Based on the

entered data of the field, the expected yield is given.

VTT contributed in developing the detection algorithm for the

Solentum’s application

5030.12.16 50

Printed visual indicators at VTT Ltd

Detecting:

• O2

• H2S

• Ethanol

• Aldehydes and ketones

Poultry freshness (H2S)

With UPM (Finland)

The development work carried out in EU project SusFoFlex “Smart and sustainable food packaging

utilizing flexible printed intelligence and materials technologies” funded by EU FP7/2007-2013 under

grant agreement no 289829 (2012-2014)

Ethanol sensor

coupled with RFID

For fresh cut fruits

Anti-tampering label INTACT

Clean Card PRO®

Clean surfaces test

with Orion Diagnostica

51

IMS beads with signal enhancers

Raman spectrum of Listeria innocua

Uusitalo, S., et al. "Detection of Listeria innocua on roll-to-roll produced SERS

substrates with gold nanoparticles." RSC Advances 6.67 (2016): 62981-62989.

Listeria detection by Surface-enhanced Raman

spectroscopy (SERS)

Low cost R2R polymer based SERS substrate

Bacteria accumulated by immunomagnetic

separation beads for enhanced signal intensity

The limit of detection in the range of ∼104 CFU ml−1

S.Z. Oo, R.Y. Chen, S. Siitonen, V. Kontturi, D.A. Eustace, J. Tuominen, S. Aikio, and M.D.B.

Optics Express Vol. 21, Issue 15, pp. 18484-18491 (2013).

5252

Roll-to-roll patterned

SERS substrates by UV-nanoimprint lithography

Production by roll-to-roll printing and vacuum deposition of metal

Roll of sensors are cut into smaller pieces that are disposable,

sensor price ~ 1€

Sensor area is about 1 cm X 1cm

Analysis off-line by Raman microscopy and on-line by fiber

connected analyzers

Sensor nanostructures Devices for analysis

S.Z. Oo, R.Y. Chen, S. Siitonen, V. Kontturi, D.A. Eustace, J. Tuominen, S. Aikio, and M.D.B. Charlton: Disposable plasmonic plastic SERS sensor.

Optics Express Vol. 21, Issue 15, pp. 18484-18491 (2013).

53

Acknowledgements

• Roope Näsi , Eija Honkavaara,

Anttoni Jaakkola et al. (fgi.fi, nls.fi)

• Jere Kaivosoja et al. (luke.fi)

• Joni Leinvuo, biomensio.com

• Jarkko Antila, spectralengines.com

• Nadine Pesonen, quanturi.com

• Edvard Krogius, grainsense.com

• VTT: Heikki Saari, Altti Akujärvi,

Leena Hakalahti, Maria Smolander,

Thea Sipiläinen-Malm, Himadri

Majumdar, Liisa Hakola, Kaarle

Jaakkola, Kaj Nummila,…

vttresearch.com

@dronefinland

philippe.monnoyer@vtt.fi

@PhMonnoyer